OpenRaiser/Intern-Atlas huggingface dataset:
Characterization, description, and considerations for the use of funding acknowledgement data in Web of Science
Venue: arXiv cs.DL 2016
Year: 2016

Funding acknowledgements found in scientific publications have been used to study the impact of funding on research since the 1970s. However, no broad scale indexation of that paratextual element was done until 2008, when Thomson Reuters Web of Science started to add funding acknowledgement information to its bibliographic records. As this new information provides a new dimension to bibliometric data that can be systematically exploited, it is important to understand the characteristics of these data and the underlying implications for their use. This paper analyses the presence and distribution of funding acknowledgement data covered in Web of Science. Our results show that prior to 2009 funding acknowledgements coverage is extremely low and therefore not reliable. Since 2008, funding information has been collected mainly for publications indexed in the Science Citation Index Expanded (SCIE); more recently (2015), inclusion of funding texts for publications indexed in the Social Science Citation Index (SSCI) has been implemented. Arts & Humanities Citation Index (AHCI) content is not indexed for funding acknowledgement data. Moreover, English-language publications are the most reliably covered. Finally, not all types of documents are equally covered for funding information indexation and only articles and reviews show consistent coverage. The characterization of the funding acknowledgement information collected by Thomson Reuters can therefore help understand the possibilities offered by the data but also their limitations.
Supervised and Unsupervised Ensembling for Knowledge Base Population
Venue: arXiv cs.CL 2016
Year: 2016

We present results on combining supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and Linking (TEDL). We demonstrate that our combined system along with auxiliary features outperforms the best performing system for both tasks in the 2015 competition, several ensembling baselines, as well as the state-of-the-art stacking approach to ensembling KBP systems. The success of our technique on two different and challenging problems demonstrates the power and generality of our combined approach to ensembling.
Cloud Instance Management and Resource Prediction For Computation-as-a-Service Platforms
Venue: arXiv cs.DC 2016
Year: 2016

Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of individual processing tasks to available cloud instances (compute units) according to availability and predetermined time-to-completion (TTC) constraints; (ii) accurate resource prediction; (iii) efficient control of the number of cloud instances servicing workloads, in order to optimize between completing workloads in a timely fashion and reducing resource utilization costs. In this paper, we propose three approaches that satisfy these properties (respectively): (i) a service rate allocation mechanism based on proportional fairness and TTC constraints; (ii) Kalman-filter estimates for resource prediction; and (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of compute units servicing workloads. The integration of our three proposals\ into a single CaaS platform is shown to provide for more than 27% reduction in Amazon EC2 spot instance cost against methods based on reactive resource prediction and 38% to 60% reduction of the billing cost against the current state-of-the-art in CaaS platforms (Amazon Lambda and Autoscale).
The Internet of Things: New Interoperability, Management and Security Challenges
Venue: arXiv cs.NI 2016
Year: 2016

The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security, management, and privacy.
Visual saliency detection: a Kalman filter based approach
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we propose a Kalman filter aided saliency detection model which is based on the conjecture that salient regions are considerably different from our "visual expectation" or they are "visually surprising" in nature. In this work, we have structured our model with an immediate objective to predict saliency in static images. However, the proposed model can be easily extended for space-time saliency prediction. Our approach was evaluated using two publicly available benchmark data sets and results have been compared with other existing saliency models. The results clearly illustrate the superior performance of the proposed model over other approaches.
On the Non-existence of certain classes of generalized bent functions
Venue: arXiv cs.IT 2016
Year: 2016

We obtain new non-existence results of generalized bent functions from \ZZ^n_q to \ZZ_q (called type [n,q]). The first case is a class of types where q=2p_1^{r_1}p_2^{r_2}. The second case contains two types [1 <= n <= 3, 2 * 31^e]$ and [1 <= n <= 7,2 * 151^e].
Probabilistic Receiver Architecture Combining BP, MF, and EP for Multi-Signal Detection
Venue: arXiv cs.IT 2016
Year: 2016

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. In this paper, we show that the MF approximation is not particularly effective for multi-signal detection. We develop a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We then develop a low-complexity variant to the proposed construction in which Gaussian BP is applied to the equalization factors. In this case, the factor graph of the joint probability distribution is divided into three subgraphs: (i) a MF subgraph comprised of the observation factors and channel estimation, (ii) a Gaussian BP subgraph which is applied to multi-signal detection, and (iii) a discrete BP subgraph which is applied to demodulation and decoding. Expectation propagation is used to approximate discrete distributions with a Gaussian distribution and links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection.
Online Truthful Mechanisms for Multi-sided Markets
Venue: arXiv cs.GT 2016
Year: 2016

The study of mechanisms for multi-sided markets has received an increasingly growing attention from the research community, and is motivated by the numerous examples of such markets on the web and in electronic commerce. Many of these examples represent dynamic and uncertain environments, and thus, require, in fact, online mechanisms. Unfortunately, as far as we know, no previously published online mechanism for a multi-sided market (or even for a double-sided market) has managed to (approximately) maximize the gain from trade, while guaranteeing desirable economic properties such as incentivizing truthfulness, voluntary participation and avoiding budget deficit. In this work we present the first online mechanism for a multi-sided market which has the above properties. Our mechanism is designed for a market setting suggested by [Feldman and Gonen (2016)]; which is motivated by the foreseeable future form of online advertising. The online nature of our setting motivated us to define a stronger notion of individual rationality, called "continuous individual rationality", capturing the natural requirement that a player should never lose either by participating in the mechanism or by not leaving prematurely. Satisfying the requirements of continuous individual rationality, together with the other economic properties our mechanism guarantees, requires the mechanism to use a novel pricing scheme where users may be paid ongoing increments during the mechanism's execution up to a pre-known maximum value. As users rarely ever get paid in reality, this pricing scheme is new to mechanism design. Nevertheless, the principle it is based on can be observed in many common real life scenarios such as executive compensation payments and company acquisition deals. We believe both our new dynamic pricing scheme concept and our strengthened notion of individual rationality are of independent interest.
Mahalanobis Distance Metric Learning Algorithm for Instance-based Data Stream Classification
Venue: arXiv cs.LG 2016
Year: 2016

With the massive data challenges nowadays and the rapid growing of technology, stream mining has recently received considerable attention. To address the large number of scenarios in which this phenomenon manifests itself suitable tools are required in various research fields. Instance-based data stream algorithms generally employ the Euclidean distance for the classification task underlying this problem. A novel way to look into this issue is to take advantage of a more flexible metric due to the increased requirements imposed by the data stream scenario. In this paper we present a new algorithm that learns a Mahalanobis metric using similarity and dissimilarity constraints in an online manner. This approach hybridizes a Mahalanobis distance metric learning algorithm and a k-NN data stream classification algorithm with concept drift detection. First, some basic aspects of Mahalanobis distance metric learning are described taking into account key properties as well as online distance metric learning algorithms. Second, we implement specific evaluation methodologies and comparative metrics such as Q statistic for data stream classification algorithms. Finally, our algorithm is evaluated on different datasets by comparing its results with one of the best instance-based data stream classification algorithm of the state of the art. The results demonstrate that our proposal is better
A global constraint for closed itemset mining
Venue: arXiv cs.AI 2016
Year: 2016

Discovering the set of closed frequent patterns is one of the fundamental problems in Data Mining. Recent Constraint Programming (CP) approaches for declarative itemset mining have proven their usefulness and flexibility. But the wide use of reified constraints in current CP approaches raises many difficulties to cope with high dimensional datasets. This paper proposes CLOSED PATTERN global constraint which does not require any reified constraints nor any extra variables to encode efficiently the Closed Frequent Pattern Mining (CFPM) constraint. CLOSED-PATTERN captures the particular semantics of the CFPM problem in order to ensure a polynomial pruning algorithm ensuring domain consistency. The computational properties of our constraint are analyzed and their practical effectiveness is experimentally evaluated.
Funding Data from Publication Acknowledgements: Coverage, Uses and Limitations
Venue: arXiv cs.DL 2016
Year: 2016

This article contributes to the development of methods for analysing research funding systems by exploring the robustness and comparability of emerging approaches to generate funding landscapes useful for policy making. We use a novel dataset of manually extracted and coded data on the funding acknowledgements of 7,510 publications representing UK cancer research in the year 2011 and compare these 'reference data' with funding data provided by Web of Science (WoS) and MEDLINE/PubMed. Findings show high recall (about 93%) of WoS funding data. By contrast, MEDLINE/PubMed data retrieved less than half of the UK cancer publications acknowledging at least one funder. Conversely, both databases have high precision (+90%): i.e. few cases of publications with no acknowledgement to funders are identified as having funding data. Nonetheless, funders acknowledged in UK cancer publications were not correctly listed by MEDLINE/PubMed and WoS in about 75% and 32% of the cases, respectively. 'Reference data' on the UK cancer research funding system are then used as a case-study to demonstrate the utility of funding data for strategic intelligence applications (e.g. mapping of funding landscape, comparison of funders' research portfolios).
Generating Semi-Synthetic Validation Benchmarks for Embryomics
Venue: arXiv cs.CV 2016
Year: 2016

Systematic validation is an essential part of algorithm development. The enormous dataset sizes and the complexity observed in many recent time-resolved 3D fluorescence microscopy imaging experiments, however, prohibit a comprehensive manual ground truth generation. Moreover, existing simulated benchmarks in this field are often too simple or too specialized to sufficiently validate the observed image analysis problems. We present a new semi-synthetic approach to generate realistic 3D+t benchmarks that combines challenging cellular movement dynamics of real embryos with simulated fluorescent nuclei and artificial image distortions including various parametrizable options like cell numbers, acquisition deficiencies or multiview simulations. We successfully applied the approach to simulate the development of a zebrafish embryo with thousands of cells over 14 hours of its early existence.
Compositional security and collateral leakage
Venue: arXiv cs.CR 2016
Year: 2016

In quantitative information flow we say that program $Q$ is "at least as secure as" $P$ just when the amount of secret information flowing from $Q$ is never more than flows from $P$, with of course a suitable quantification of "flow". This secure-refinement order $\sqsubseteq$ is compositional just when $P{\sqsubseteq}Q$ implies ${\cal C}(P){\sqsubseteq}{\cal C}(Q)$ for any context ${\cal C}$, again with a suitable definition of "context". Remarkable however is that leaks caused by executing $P,Q$ might not be limited to their declared variables: they might impact correlated secrets in variables declared and initialised in some broader context to which $P,Q$ do not refer even implicitly. We call such leaks collateral because their effect is felt in domains of which (the programmers of) $P, Q$ might be wholly unaware: our inspiration is the "Dalenius" phenomenon for statistical databases. We show that a proper treatment of these collateral leaks is necessary for a compositional program semantics for read/write "open" programs. By adapting a recent Hidden-Markov denotational model for non-interference security, so that it becomes "collateral aware", we give techniques and examples (e.g.\ public-key encryption) to show how collateral leakage can be calculated and then bounded in its severity.
Time-Frequency analysis via the Fourier Representation
Venue: arXiv cs.IT 2016
Year: 2016

The nonstationary nature of signals and nonlinear systems require the time-frequency representation. In time-domain signal, frequency information is derived from the phase of the Gabor's analytic signal which is practically obtained by the inverse Fourier transform. This study presents time-frequency analysis by the Fourier transform which maps the time-domain signal into the frequency-domain. In this study, we derive the time information from the phase of the frequency-domain signal and obtain the time-frequency representation. In order to obtain the time information in Fourier domain, we define the concept of `frequentaneous time' which is frequency derivative of phase. This is very similar to the group delay, which is also defined as frequency derivative of phase and it provide physical meaning only when it is positive. The frequentaneous time is always positive or negative depending upon whether signal is defined for only positive or negative times, respectively. If a signal is defined for both positive and negative times, then we divide the signal into two parts, signal for positive times and signal for negative times. The proposed frequentaneous time and Fourier transform based time-frequency distribution contains only those frequencies which are present in the Fourier spectrum. Simulations and numerical results, on many simulated as well as read data, demonstrate the efficacy of the proposed method for the time-frequency analysis of a signal.
Optical Onion Routing
Venue: arXiv cs.CR 2016
Year: 2016

As more and more data is transmitted in the configurable optical layer, whereby all optical switches forward packets without electronic layers involved, we envision privacy as the intrinsic property of future optical networks. In this paper, we propose Optical Onion Routing (OOR) routing and forwarding techniques, inspired by the onion routing in the Internet layer, the best known realization of anonymous communication today, but designed with specific features innate to optical networks. We propose to design the optical anonymization network system with a new optical anonymization node architecture, including the optical components and their electronic counterparts to realize layered encryption. We propose modification to the secret key generation using Linear Feedback Shift Register (LFSR), able to utilize different primitive irreducible polynomials, and the usage optical XOR operation as encryption, an important optical technology coming of age. We prove formally that, for the proposed encryption techniques and distribution of secret information, the optical onion network is perfectly private and secure. The paper aims at providing practical foundations for privacy-enhancing optical network technologies.
Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping
Venue: arXiv cs.AI 2016
Year: 2016

2048 is an engaging single-player, nondeterministic video puzzle game, which, thanks to the simple rules and hard-to-master gameplay, has gained massive popularity in recent years. As 2048 can be conveniently embedded into the discrete-state Markov decision processes framework, we treat it as a testbed for evaluating existing and new methods in reinforcement learning. With the aim to develop a strong 2048 playing program, we employ temporal difference learning with systematic n-tuple networks. We show that this basic method can be significantly improved with temporal coherence learning, multi-stage function approximator with weight promotion, carousel shaping, and redundant encoding. In addition, we demonstrate how to take advantage of the characteristics of the n-tuple network, to improve the algorithmic effectiveness of the learning process by i) delaying the (decayed) update and applying lock-free optimistic parallelism to effortlessly make advantage of multiple CPU cores. This way, we were able to develop the best known 2048 playing program to date, which confirms the effectiveness of the introduced methods for discrete-state Markov decision problems.
Defragmentation-as-a-Service (DaaS): How beneficial is it?
Venue: arXiv cs.NI 2016
Year: 2016

We analyze Defragmentation-as-a-Service (DaaS) in elastic optical networks and show that the positive effect of defragmentation depends on the rate at which it is performed, load (or, call arrival rates) and the available resources.
FEC-Assisted Parallel Decoding of Polar Coded Frames: Design Considerations
Venue: arXiv cs.IT 2016
Year: 2016

This paper deals with two main issues regarding the short polar codes: the potential of FEC-assisted decoding and optimal code concatenation strategies under various design scenarios. Code concatenation and FEC-assisted decoding are presented systematically, assuming a packetized system. It is shown that FEC-assisted decoding can improve frame error rate of successive cancellation decoding arbitrarily, at the expense of some coding rate loss and decoding complexity linearly increasing with the number of codewords in the frame. This is compared with list decoding whose complexity grows linearly with the list size as well as the number of codewords. Thereafter, the frame construction procedure and decoding algorithm are developed in a realistic framework. Taking into consideration the effective throughput of the transmission protocol, the problem of optimal design of concatenated codes is formulated under polar code length, frame length and target frame-success-rate constraints. Simulations are performed assuming both additive white Gaussian noise and Rayleigh fading channels. It is shown that the divide-concatenate strategy for long frames does not lead to any considerable gain. It is also shown that the performance of FEC-assisted decoding of frames is improved as the frame length increases while the conventional successive cancellation decoding undergoes a dramatic performance loss.
Numerical solution of a parabolic system in air pollution
Venue: arXiv cs.CE 2016
Year: 2016

An air pollution model is generally described by a system of PDEs on unbounded domain. Transformation of the independent variable is used to convert the problem for nonlinear air pollution on finite computational domain. We investigate the new, degenerated parabolic problem in Sobolev spaces with weights for well-posedness and positivity of the solution. Then we construct a fitted finite volume difference scheme. Some results from computations are presented.
Two-grid algorithms for singularly perturbed reaction-diffusion problems on layer adapted meshes
Venue: arXiv cs.CE 2016
Year: 2016

We propose a new two-grid approach based on Bellman-Kalaba quasilinearization and Axelsson-Xu finite element two-grid method for the solution of singularly perturbed reaction-diffusion equations. The algorithms involve solving one inexpensive problem on coarse grid and solving on fine grid one linear problem obtained by quasilinearization of the differential equation about an interpolant of the computed solution on the coarse grid. Different meshes (of Bakhvalov, Shishkin and Vulanović types) are examined. All the schemes are uniformly convergent with respect to the small parameter. We show theoretically and numerically that the global error of the two-grid method is the same as of the nonlinear problem solved directly on the fine layer-adapted mesh.
Theoretically Optimal Datalog Rewritings for OWL 2 QL Ontology-Mediated Queries
Venue: arXiv cs.LO 2016
Year: 2016

We show that, for OWL 2 QL ontology-mediated queries with (i) ontologies of bounded depth and conjunctive queries of bounded treewidth, (ii) ontologies of bounded depth and bounded-leaf tree-shaped conjunctive queries, and (iii) arbitrary ontologies and bounded-leaf tree-shaped conjunctive queries, one can construct and evaluate nonrecursive datalog rewritings by, respectively, LOGCFL, NL and LOGCFL algorithms, which matches the optimal combined complexity.